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21 results (View BibTeX file of all listed publications)

2017

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Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

ei

PDF [BibTex]

2017

ei Peters, J., Janzing, D., Schölkopf, B. Elements of Causal Inference - Foundations and Learning Algorithms Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

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Computing with Uncertainty

Hennig, P.

2017 (mpi_year_book)

Abstract
Machine learning requires computer hardware to reliable and efficiently compute estimations for ever more complex and fundamentally incomputable quantities. A research team at MPI for Intelligent Systems in Tübingen develops new algorithms which purposely lower the precision of computations and return an explicit measure of uncertainty over the correct result alongside the estimate. Doing so allows for more flexible management of resources, and increases the reliability of intelligent systems.

link (url) DOI [BibTex]

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Biomechanics and Locomotion Control in Legged Animals and Legged Robots

Sproewitz, A., Heim, S.

2017 (mpi_year_book)

Abstract
An animal's running gait is dynamic, efficient, elegant, and adaptive. We see locomotion in animals as an orchestrated interplay of the locomotion apparatus, interacting with its environment. The Dynamic Locomotion Group at the Max Planck Institute for Intelligent Systems in Stuttgart develops novel legged robots to decipher aspects of biomechanics and neuromuscular control of legged locomotion in animals, and to understand general principles of locomotion.

link (url) DOI [BibTex]

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Mobile Microrobotics

Sitti, M.

Mobile Microrobotics, The MIT Press, Cambridge, MA, 2017 (book)

Abstract
Progress in micro- and nano-scale science and technology has created a demand for new microsystems for high-impact applications in healthcare, biotechnology, manufacturing, and mobile sensor networks. The new robotics field of microrobotics has emerged to extend our interactions and explorations to sub-millimeter scales. This is the first textbook on micron-scale mobile robotics, introducing the fundamentals of design, analysis, fabrication, and control, and drawing on case studies of existing approaches. The book covers the scaling laws that can be used to determine the dominant forces and effects at the micron scale; models forces acting on microrobots, including surface forces, friction, and viscous drag; and describes such possible microfabrication techniques as photo-lithography, bulk micromachining, and deep reactive ion etching. It presents on-board and remote sensing methods, noting that remote sensors are currently more feasible; studies possible on-board microactuators; discusses self-propulsion methods that use self-generated local gradients and fields or biological cells in liquid environments; and describes remote microrobot actuation methods for use in limited spaces such as inside the human body. It covers possible on-board powering methods, indispensable in future medical and other applications; locomotion methods for robots on surfaces, in liquids, in air, and on fluid-air interfaces; and the challenges of microrobot localization and control, in particular multi-robot control methods for magnetic microrobots. Finally, the book addresses current and future applications, including noninvasive medical diagnosis and treatment, environmental remediation, and scientific tools.

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Mobile Microrobotics By Metin Sitti - Chapter 1 (PDF) link (url) [BibTex]

pi Sitti, M. Mobile Microrobotics Mobile Microrobotics, The MIT Press, Cambridge, MA, 2017 (book)

Mobile Microrobotics By Metin Sitti - Chapter 1 (PDF) link (url) [BibTex]

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New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.

Dagstuhl Reports, 6(11):142-167, 2017 (book)

ei pn

DOI [BibTex]

ei pn Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B. New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481) Dagstuhl Reports, 6(11):142-167, 2017 (book)

DOI [BibTex]

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Design of a visualization scheme for functional connectivity data of Human Brain

Bramlage, L.

Hochschule Osnabrück - University of Applied Sciences, 2017 (thesis)

sf

Bramlage_BSc_2017.pdf [BibTex]

2016

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Interface-controlled phenomena in nanomaterials

Mittemeijer, Eric J.; Wang, Zumin

2016 (mpi_year_book)

Abstract
Nanosized material systems characteristically exhibit an excessively high internal interface density. A series of previously unknown phenomena in nanomaterials have been disclosed that are fundamentally caused by the presence of interfaces. Thus anomalously large and small lattice parameters in nanocrystalline metals, quantum stress oscillations in growing nanofilms, and extraordinary atomic mobility at ultralow temperatures have been observed and explained. The attained understanding for these new phenomena can lead to new, sophisticated applications of nanomaterials in advanced technologies.

link (url) [BibTex]

2016

Mittemeijer, Eric J.; Wang, Zumin Interface-controlled phenomena in nanomaterials 2016 (mpi_year_book)

link (url) [BibTex]

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Robots learn how to see

Geiger, A.

2016 (mpi_year_book)

Abstract
Autonomous vehicles and intelligent service robots could soon contribute to making our lives more pleasant and secure. However, for autonomous operation such systems first need to learn the perception process itself. This involves measuring distances and motions, detecting objects and interpreting the threedimensional world as a whole. While humans perceive their environment with seemingly little efforts, computers first need to be trained for these tasks. Our research is concerned with developing mathematical models which allow computers to robustly perceive their environment.

link (url) DOI [BibTex]

2014

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Advanced Structured Prediction

Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H.

Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

Abstract
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

ps

publisher link (url) [BibTex]

2014

ps Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H. Advanced Structured Prediction Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

publisher link (url) [BibTex]

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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

ei

DOI [BibTex]

ei Kober, J., Peters, J. Learning Motor Skills: From Algorithms to Robot Experiments 97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

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Exploring complex diseases with intelligent systems

Borgwardt, K.

2014 (mpi_year_book)

Abstract
Physicians are collecting an ever increasing amount of data describing the health state of their patients. Is new knowledge about diseases hidden in this data, which could lead to better therapies? The field of Machine Learning in Biomedicine is concerned with the development of approaches which help to gain such insights from massive biomedical data.

link (url) [BibTex]

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The cellular life-death decision – how mitochondrial membrane proteins can determine cell fate

García-Sáez, Ana J.

2014 (mpi_year_book)

Abstract
Living organisms have a very effective method for eliminating cells that are no longer needed: programmed death. Researchers in the group of Ana García Sáez work with a protein called Bax, a key regulator of apoptosis that creates pores with a flexible diameter inside the outer mitochondrial membrane. This step inevitably triggers the final death of the cell. These insights into the role of important key enzymes in setting off apoptosis could provide useful for developing drugs that can directly influence apoptosis.

link (url) [BibTex]

2013

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Perceiving Systems – Computers that see

Gehler, P. V.

2013 (mpi_year_book)

Abstract
Our research goal is to define in a mathematical precise way how visual perception works. We want to describe how intelligent systems understand images. To this end we study probabilistic models and statistical learning. Encoding prior knowledge about the world is complemented with automatic learning from training data. One aspect is being able to identify physical factors in images, such as lighting, geometry, and materials. Furthermore we want to automatically recognize and give names to objects and persons in images and understand the scene as a whole.

link (url) [BibTex]

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Being small, being smart

Liu, Na

2013 (mpi_year_book)

Abstract
Metallic nanostructures feature plasmonic resonances which spatially confine light on the nanometer scale. In the ultimate limit of a single nanostructure, the electromagnetic field can be strongly concentrated in a volume of only a few hundred nm3 or less. We utilize such plasmonic focusing for hydrogen detection at the single particle level, which avoids any inhomogeneous broadening and statistical effects that would occur in sensors based on nanoparticle ensembles. This concept paves the road towards the observation of single catalytic processes in nanoreactors.

link (url) [BibTex]

Liu, Na Being small, being smart 2013 (mpi_year_book)

link (url) [BibTex]

2012

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Brain-computer interfaces – a novel type of communication

Grosse-Wentrup, M.

2012 (mpi_year_book)

Abstract
Brain-computer interfaces (BCIs) provide a new means of communication that does not rely on volitional muscle control. This may provide the capability to locked-in patients, e.g., those suffering from amyotrophic lateral sclerosis, to maintain interactions with their environment. Besides providing communication capabilities to locked-in patients, BCIs may further prove to have a beneficial impact on stroke rehabilitation. In this article, the state-of-the-art of BCIs is reviewed and current research questions are discussed.

link (url) [BibTex]

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From artificial flagella to medical microbots – the start of a "phantastic voyage"

Fischer, P.

2012 (mpi_year_book)

Abstract
There have been numerous speculations in scientific publications and the popular media about wirelessly controlled microrobots (microbots) navigating the human body. Such micro-agents could revolutionize minimally invasive medical procedures. Using physical vapor deposition we grow billions of micron-sized colloidal screw-propellers on a wafer. These chiral mesoscopic screws can be magnetized and moved through solution under computer control. The screw-propellers resemble artificial flagella and are the only ‘microbots’ to date that can be fully controlled in solution at micron length scales.

link (url) [BibTex]

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The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots

Der, R., Martius, G.

Springer, Berlin Heidelberg, 2012 (book)

Abstract
Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self-determined, individual development in a playful and obviously embodiment-related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world.

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link (url) [BibTex]

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Consumer Depth Cameras for Computer Vision - Research Topics and Applications

Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K.

Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

ps

workshop publisher's site [BibTex]

ps Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. Consumer Depth Cameras for Computer Vision - Research Topics and Applications Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

workshop publisher's site [BibTex]

2006

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Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

ei

Web [BibTex]

2006

ei Chapelle, O., Schölkopf, B., Zien, A. Semi-Supervised Learning pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Web [BibTex]

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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

ei

Web [BibTex]

ei Rasmussen, CE., Williams, CKI. Gaussian Processes for Machine Learning pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Web [BibTex]